Chapter title |
Predicting Pseudouridine Sites with Porpoise.
|
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Chapter number | 10 |
Book title |
Computational Epigenomics and Epitranscriptomics
|
Published in |
Methods in molecular biology, January 2023
|
DOI | 10.1007/978-1-0716-2962-8_10 |
Pubmed ID | |
Book ISBNs |
978-1-07-162961-1, 978-1-07-162962-8
|
Authors |
Guo, Xudong, Li, Fuyi, Song, Jiangning |
Abstract |
Pseudouridine is a ubiquitous RNA modification and plays a crucial role in many biological processes. However, it remains a challenging task to identify pseudouridine sites using expensive and time-consuming experimental research. To this end, we present Porpoise, a computational approach to identify pseudouridine sites from RNA sequence data. Porpoise builds on a stacking ensemble learning framework with several informative features and achieves competitive performance compared with state-of-the-art approaches. This protocol elaborates on step-by-step use and execution of the local stand-alone version and the webserver of Porpoise. In addition, we also provide a general machine learning framework that can help identify the optimal stacking ensemble learning model using different combinations of feature-based features. This general machine learning framework can facilitate users to build their pseudouridine predictors using their in-house datasets. |
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Members of the public | 1 | 100% |